Consider an interference channel for multiuser downlink transmission,
where the independent data streams are sent to some intended receivers
simultaneously, one classical target of the system design is to achieve
high total throughput (i.e., sum-rates) [1]}, [2]}.
In [3]}, the weighted sum-rate (WSR) maximization
problem for a single-hop RIS-aided multi-user MISO systems was considered
where the phase-shift matrix at the RIS and the active beamforming
matrix at the base station (BS) are jointly designed. The WSR maximization
problem is nonconvex due to the non-convexity of the objective and
the constraints, for which two different approaches are proposed in
[3]}, namely, an alternating minimization (AM)
algorithm leveraging WMMSE [5]} and Riemannian
conjugate gradient [6]}, and the block coordinate
descent (BCD) method aided by the fractional programming (FP) approach
[7]}. However, convergence of these approaches
may not be favorable in practice. For the WMMSE-based AM method, it
inherits a double-loop nature which may invoke many iterations to
converge resulting in high per-iteration computational complexity.
For the FP-based BCD method, it relies on the manifold structure of
the continuous phase constraint making it become lame in the more
practical discrete phase setting [8]}. As for the
multi-hop RIS-aided systems, to the best of our knowledge, there only
exists a deep reinforcement learning-based approach [9]}
and its performance highly relies on the carefully chosen initializations
which call for other iterative algorithms.
